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Market Facilitation Index (MFI) - Color Phases+Key Features:- 1.4-Color Phase Detection: -Automatically colors columns based on Bill Williams' original phases -Floating labels show current market phase 2.Smart Moving Average: -Choose between EMA/SMA/WMA for trend filtering -Helps identify MFI directionality 3.Built-in Alerts: -Get notified for critical phases (Green/Fake) -Easily extendable with custom alert conditions 4.Volume Safeguard: -Handles zero-volume bars gracefully -Adjustable lookback period for volume comparisons +Usage Guide: Phase/Color Trading Implication Green -Add to positions, trend continuation Fade -Prepare for reversals/retracements Squat -Set breakout orders, reduce exposure Fake -Close positions, counter-trade
Indicador Pine Script®
por jimilthakor103
2
MFI+How to Read MFI 1. MFI Value Direction: Scenario Interpretation MFI Rising Market is moving efficiently (price advancing with participation) MFI Falling Market inefficiency (price struggling to move despite volume) 2. Volume Relationship: Volume + MFI Combination Market Phase (Bill Williams) What It Means MFI ↑ + Volume ↑ Green Phase Strong trend continuation (institutional participation) MFI ↓ + Volume ↑ Fade Phase Weak momentum (traders fading moves, potential reversal) MFI ↓ + Volume ↓ Squat Phase Consolidation (market indecision, prepare for breakout) MFI ↑ + Volume ↓ Fake Phase False breakout (price moving without volume confirmation) +Practical Trading Signals A. Trend Confirmation Valid Trend: Rising MFI + Rising Volume → Trade in trend direction False Trend: Rising MFI + Falling Volume → Avoid chasing moves B. Breakout Validation // Example: Bullish Breakout Confirmation if (high > previous_high) and (mfi > mfi ) and (volume > volume ) enterLong() C. Divergence Detection Bearish Divergence: Price makes new highs but MFI fails to confirm Bullish Divergence: Price makes new lows but MFI holds above prior lows +Key Trading Strategies: 1.Green Phase Trading: -Enter trades in trend direction -Add to positions on pullbacks 2.Fake Phase Reversals: -Look for price spikes with declining volume -Counter-trade when MFI diverges from price 3.Squat Phase Preparation: -Set buy-stop above consolidation range -Place sell-stop below support MFI vs. Traditional Volume Indicators Feature MFI OBV/Volume Oscillators Focus Price efficiency Pure volume flow Best Use Breakout validation Trend confirmation Noise Handling Filters low-conviction moves Requires additional filters
Indicador Pine Script®
por jimilthakor103
Supply & Demand with Candle SignalsUnlock the power of Supply & Demand Zones combined with high-probability Bullish/Bearish Engulfing patterns to spot strong market reversals and trends. This strategy helps identify key price levels where major market moves are likely to occur. By using Engulfing candlesticks within these zones, you can make more informed and accurate trading decisions, enhancing your chances of success. Ideal for traders looking for a robust technical approach to maximize market opportunities.
Indicador Pine Script®
por gokulkrish80
Hash Rate to Market Cap ChannelWe can visualize market sentiment towards security of the Bitcoin network by dividing the Marketcap by Hashrate. This can determine under and overvaluation of the network itself per dollar. The value is then normalized over the lookback period to create an oscillating channel. SETTINGS Lookback Period - used for calculating the normalization and how far back to look for highs and lows. HMA Smoothing Length - Faster moving average to smooth out the curves Channel Width - visually change the channel scale Bear/Bull Value - Under and Overvaluation Use Log Scaling - adjusts the channel visuals for log scaled charts
Indicador Pine Script®
por OrionAlgo
3
Buy/Sell EMA Trend Filter v6Buy/Sell EMA Trend Filter v6 This indicator provides a comprehensive trading system based on EMA crossovers with trend filtering for TradingView. It's designed to identify high-probability buy and sell signals by combining short-term crossovers with longer-term trend direction confirmation. Key Features: EMA Crossover System: Uses fast and slow EMAs (9 and 21 by default) to generate initial signals Trend Filtering: Confirms signals with longer-term trend direction (50 and 200 EMAs) Automatic TP/SL Calculation: Displays clear take profit and stop loss levels based on fixed risk points Visual Alerts: Clear buy/sell markers at the point of signal with detailed labels Risk Management: Pre-calculated risk-to-reward setup (default 1:2 ratio) How It Works: Buy Signal: When the fast EMA crosses above the slow EMA while the 50 EMA is above the 200 EMA (bullish trend) Sell Signal: When the fast EMA crosses below the slow EMA while the 50 EMA is below the 200 EMA (bearish trend) Customizable Parameters: Fast EMA period (default: 9) Slow EMA period (default: 21) Trend EMA periods (default: 50 and 200) Fixed risk in points (default: 20) Reward ratio (default: 2.0) The indicator displays clear entry points with predefined stop loss and take profit levels, making it ideal for traders looking for a systematic approach to the markets. Perfect for both day trading and swing trading timeframes. This tool combines both trend following and momentum principles to filter out low-probability trades and focus on high-quality setups where the trend and momentum align.
Indicador Pine Script®
por ScalperBerlin
Stochastic Order Flow Momentum [ScorsoneEnterprises]This indicator implements a stochastic model of order flow using the Ornstein-Uhlenbeck (OU) process, combined with a Kalman filter to smooth momentum signals. It is designed to capture the dynamic momentum of volume delta, representing the net buying or selling pressure per bar, and highlight potential shifts in market direction. The volume delta data is sourced from TradingView’s built-in functionality: www.tradingview.com For a deeper dive into stochastic processes like the Ornstein-Uhlenbeck model in financial contexts, see these research articles: arxiv.org and arxiv.org The SOFM tool aims to reveal the momentum and acceleration of order flow, modeled as a mean-reverting stochastic process. In markets, order flow often oscillates around a baseline, with bursts of buying or selling pressure that eventually fade—similar to how physical systems return to equilibrium. The OU process captures this behavior, while the Kalman filter refines the signal by filtering noise. Parameters theta (mean reversion rate), mu (mean level), and sigma (volatility) are estimated by minimizing a squared-error objective function using gradient descent, ensuring adaptability to real-time market conditions. How It Works The script combines a stochastic model with signal processing. Here’s a breakdown of the key components, including the OU equation and supporting functions. // Ornstein-Uhlenbeck model for volume delta ou_model(params, v_t, lkb) => theta = clamp(array.get(params, 0), 0.01, 1.0) mu = clamp(array.get(params, 1), -100.0, 100.0) sigma = clamp(array.get(params, 2), 0.01, 100.0) error = 0.0 v_pred = array.new(lkb, 0.0) array.set(v_pred, 0, array.get(v_t, 0)) for i = 1 to lkb - 1 v_prev = array.get(v_pred, i - 1) v_curr = array.get(v_t, i) // Discretized OU: v_t = v_{t-1} + theta * (mu - v_{t-1}) + sigma * noise v_next = v_prev + theta * (mu - v_prev) array.set(v_pred, i, v_next) v_curr_clean = na(v_curr) ? 0 : v_curr v_pred_clean = na(v_next) ? 0 : v_next error := error + math.pow(v_curr_clean - v_pred_clean, 2) error The ou_model function implements a discretized Ornstein-Uhlenbeck process: v_t = v_{t-1} + theta (mu - v_{t-1}) The model predicts volume delta (v_t) based on its previous value, adjusted by the mean-reverting term theta (mu - v_{t-1}), with sigma representing the volatility of random shocks (approximated in the Kalman filter). Parameters Explained The parameters theta, mu, and sigma represent distinct aspects of order flow dynamics: Theta: Definition: The mean reversion rate, controlling how quickly volume delta returns to its mean (mu). Constrained between 0.01 and 1.0 (e.g., clamp(array.get(params, 0), 0.01, 1.0)). Interpretation: A higher theta indicates faster reversion (short-lived momentum), while a lower theta suggests persistent trends. Initial value is 0.1 in init_params. In the Code: In ou_model, theta scales the pull toward \mu, influencing the predicted v_t. Mu: Definition: The long-term mean of volume delta, representing the equilibrium level of net buying/selling pressure. Constrained between -100.0 and 100.0 (e.g., clamp(array.get(params, 1), -100.0, 100.0)). Interpretation: A positive mu suggests a bullish bias, while a negative mu indicates bearish pressure. Initial value is 0.0 in init_params. In the Code: In ou_model, mu is the target level that v_t reverts to over time. Sigma: Definition: The volatility of volume delta, capturing the magnitude of random fluctuations. Constrained between 0.01 and 100.0 (e.g., clamp(array.get(params, 2), 0.01, 100.0)). Interpretation: A higher sigma reflects choppier, noisier order flow, while a lower sigma indicates smoother behavior. Initial value is 0.1 in init_params. In the Code: In the Kalman filter, sigma contributes to the error term, adjusting the smoothing process. Summary: theta: Speed of mean reversion (how fast momentum fades). mu: Baseline order flow level (bullish or bearish bias). sigma: Noise level (variability in order flow). Other Parts of the Script Clamp A utility function to constrain parameters, preventing extreme values that could destabilize the model. ObjectiveFunc Defines the objective function (sum of squared errors) to minimize during parameter optimization. It compares the OU model’s predicted volume delta to observed data, returning a float to be minimized. How It Works: Calls ou_model to generate predictions, computes the squared error for each timestep, and sums it. Used in optimization to assess parameter fit. FiniteDifferenceGradient Calculates the gradient of the objective function using finite differences. Think of it as finding the "slope" of the error surface for each parameter. It nudges each parameter (theta, mu, sigma) by a small amount (epsilon) and measures the change in error, returning an array of gradients. Minimize Performs gradient descent to optimize parameters. It iteratively adjusts theta, mu, and sigma by stepping down the "hill" of the error surface, using the gradients from FiniteDifferenceGradient. Stops when the gradient norm falls below a tolerance (0.001) or after 20 iterations. Kalman Filter Smooths the OU-modeled volume delta to extract momentum. It uses the optimized theta, mu, and sigma to predict the next state, then corrects it with observed data via the Kalman gain. The result is a cleaner momentum signal. Applied After initializing parameters (theta = 0.1, mu = 0.0, sigma = 0.1), the script optimizes them using volume delta data over the lookback period. The optimized parameters feed into the Kalman filter, producing a smoothed momentum array. The average momentum and its rate of change (acceleration) are calculated, though only momentum is plotted by default. A rising momentum suggests increasing buying or selling pressure, while a flattening or reversing momentum indicates fading activity. Acceleration (not plotted here) could highlight rapid shifts. Tool Examples The SOFM indicator provides a dynamic view of order flow momentum, useful for spotting directional shifts or consolidation. Low Time Frame Example: On a 5-minute chart of SEED_ALEXDRAYM_SHORTINTEREST2:NQ , a rising momentum above zero with a lookback of 5 might signal building buying pressure, while a drop below zero suggests selling dominance. Crossings of the zero line can mark transitions, though the focus is on trend strength rather than frequent crossovers. High Time Frame Example: On a daily chart of NYSE:VST , a sustained positive momentum could confirm a bullish trend, while a sharp decline might warn of exhaustion. The mean-reverting nature of the OU process helps filter out noise on longer scales. It doesn’t make the most sense to use this on a high timeframe with what our data is. Choppy Markets: When momentum oscillates near zero, it signals indecision or low conviction, helping traders avoid whipsaws. Larger deviations from zero suggest stronger directional moves to act on, this is on $STT. Inputs Lookback: Users can set the lookback period (default 5) to adjust the sensitivity of the OU model and Kalman filter. Shorter lookbacks react faster but may be noisier; longer lookbacks smooth more but lag slightly. The user can also specify the timeframe they want the volume delta from. There is a default way to lower and expand the time frame based on the one we are looking at, but users have the flexibility. No indicator is 100% accurate, and SOFM is no exception. It’s an estimation tool, blending stochastic modeling with signal processing to provide a leading view of order flow momentum. Use it alongside price action, support/resistance, and your own discretion for best results. I encourage comments and constructive criticism.
Indicador Pine Script®
por ScorsoneEnterprises
Trendline Breakouts With Targets [ Chartprime ]ITS COPIED FROM TBT WITH TARGETS What's added: STOP LOSS IS VISIBLE. CAN ADD ALERTS FOR BUY AND SELL SIGNALS. The Trendline Breakouts With Targets and visible stoploss indicator is meticulously crafted to improve trading decision-making by pinpointing trendline breakouts and breakdowns through pivot point analysis. Here's a comprehensive look at its primary functionalities: Upon the occurrence of a breakout or breakdown, a signal is meticulously assessed against a false signal condition/filter, after which the indicator promptly generates a trading signal. Additionally, it conducts precise calculations to determine potential target levels and then exhibits them graphically on the price chart. 🔷Key Features: 🔸Trendline Drawing: The indicator automatically plots trendlines based on significant pivot points and wick data, visually representing the prevailing trend.
Indicador Pine Script®
por priyapb00039
1